Most of the stemmers available in Elasticsearch are algorithmic in that they
apply a series of rules to a word in order to reduce it to its root form, such
as stripping the final s or es from plurals. They don’t have to know
anything about individual words in order to stem them.

These algorithmic stemmers have the advantage that they are available out of
the box, are fast, use little memory, and work well for regular words. The
downside is that they don’t cope well with irregular words like be, are,
and am, or mice and mouse.

One of the earliest stemming algorithms is the Porter stemmer for English,
which is still the recommended English stemmer today. Martin Porter
subsequently went on to create the
Snowball language for creating stemming
algorithms, and a number of the stemmers available in Elasticsearch are
written in Snowball.

The kstem token filter is a stemmer
for English which combines the algorithmic approach with a built-in
dictionary. The dictionary contains a list of root words and exceptions in
order to avoid conflating words incorrectly. kstem tends to stem less
aggressively than the Porter stemmer.

While you can use the
porter_stem or
kstem token filter directly, or
create a language-specific Snowball stemmer with the
snowball token filter, all of the
algorithmic stemmers are exposed via a single unified interface:
the stemmer token filter, which
accepts the language parameter.

For instance, perhaps you find the default stemmer used by the english
analyzer to be too aggressive and you want to make it less aggressive.
The first step is to look up the configuration for the english analyzer
in the language analyzers
documentation, which shows the following:

The keyword_marker token filter lists words that should not be
stemmed. This defaults to the empty list.

The english analyzer uses two stemmers: the possessive_english
and the english stemmer. The possessive stemmer removes 's
from any words before passing them on to the english_stop,
english_keywords, and english_stemmer.

Having reviewed the current configuration, we can use it as the basis for
a new analyzer, with the following changes:

Change the english_stemmer from english (which maps to the
porter_stem token filter)
to light_english (which maps to the less aggressive
kstem token filter).

Add the asciifolding token filter to
remove any diacritics from foreign words.

Remove the keyword_marker token filter, as we don’t need it.
(We discuss this in more detail in Controlling Stemming.)